Learning Interactions of Local and Non-Local Phonotactic Constraints from Positive Input

This paper proposes a grammatical inference algorithm to learn a formal class of input-sensitive tier-based strictly local languages across multiple tiers from positive data only, when the locality of the tier-constraints and of the tier-projection function is set to two (MITSL-2,2; De Santo and Graf, 2019). We then conduct simulations showing that the algorithm succeeds in learning MITSL-2,2 from an initial set of artificial languages.

• Natural Language Processing